Enterprises are facing an 80% failure rate for AI agents in complex tasks because these systems lack the deep understanding required to navigate established legacy environments and existing internal systems. In this episode, Eran Yahav, CTO and co-founder at Tabnine, outlines how an enterprise context engine acts as a persistent memory and mapping layer that onboards AI systems into specific business logic, security perimeters, and organizational dependencies. The conversation highlights how this infrastructure can double agent success rates and reduce token costs by 80% while allowing technical leaders to establish swim lanes that ensure agents operate reliably within complex software architectures. This episode is sponsored by Tabnine. Learn how brands work with Emerj and other Emerj Media options at go.emerj.com/partner. Want to share your AI adoption story with executive peers? Click go.emerj.com/expert for more information and to be a potential future guest on the 'AI in Business' podcast!

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